本論文在解決無線充電通訊網路中，能源消耗最小化的問題。我們考慮到提供電能的混合存取點有電量的限制，在滿足網路中所有無線裝置的吞吐量後，透過時間分配的方式，最佳化HAP以及無線裝置的傳輸時間，使網路的能耗為最低。本論文採用TDMA的傳輸模式，無線裝置根據HTT (Harvest-Then-Transmit) 協議進行能源的獲取以及資料的傳輸。本論文提出DT-SWIPT (Different Target Simultaneous Wireless Information and Power Transfer)機制，讓無線裝置在完成資料的傳輸後，可以將多餘的電能分享給網路中其他尚未傳輸的無線裝置，進一步舒緩網路中的能源開銷。本論文除了提出DT-SWIPT的訊框結構之外，也考慮到HAP在接收無線裝置傳輸的資料時，其資料的訊號功率與雜訊的訊雜比必需滿足SNR (Signal-to-noise ratio)的門檻值，以確保HAP可以解析該筆訊號。我們將能源消耗最小化之問題轉換成非線性規劃模型，並透過SQP (Sequential Quadratic Programming)求出最佳之時間分配。由於最佳化模型計算的時間通常較長，因此我們將原始問題簡化，並提出一個Heristic的作法。在Heristic的作法中，我們根據Linear Search設計演算法，透過迭代的方式找到問題的近似解，並證明了其時間複雜度為O(nlog⁡n)。最後，透過模擬分析顯示本論文的方法可以有效的減少網路中的能耗，並保證所有無線裝置傳輸至HAP時之訊號的SNR值。

This paper addresses the energy consumption minimization (ECm) problem in Wireless Powered Communication Networks (WPCNs). We take the energy limitation of HAP (Hybrid Access Point) into account and optimize the transmission time of HAP and wireless stations while meeting the throughput demand of all wireless stations in the network. The TDMA protocol is adopted, and the wireless station obtains energy and transmits data according to the HTT (Harvest then transmit) protocol. We propose the DT-SWIPT (Different Target Simultaneous Wireless Information and Power Transfer) mechanism, which allows wireless stations to share the excess energy to other wireless stations that have not yet been transmitted after completing the data transmission. Therefore, DT-SWIPT can reduce the energy consumption of the network. In addition to proposing the frame structure of DT-SWIPT, this paper also considers that when HAP receives data transmitted by wireless stations, the signal-to-noise ratio of the signal power of the data to the power of the noise must meet the SNR threshold to ensure that HAP can decode the signal. We transform the ECm problem into a nonlinear programming model. Then, we find the optimal time allocation through SQP (sequential quantitative programming). Since the calculation time of the optimization model is usually longer, we simplify the ECm problem and propose a Heuristic Method. In the Heuristic Method, we design an algorithm based on Linear Search, find an approximate solution to the problem through iteration. Moreover, we prove that the time complexity of the Algorithm is O(n log⁡n). Finally, simulation analysis shows that the method proposed in this paper can effectively reduce the energy consumption and ensure the SNR value of the signal when all wireless stations transmit to the HAP.

第1章	Introduction	1
第2章	Preliminaries	7
2.1 Network Model	7
2.2 SWIPT v.s. DT-SWIPT	8
2.3 SNR threshold of HAP received signal	12
第3章	DT-SWIPT Assisted Time Allocation for Energy Consumption Minimization	14
3.1 Problem Formulation	14
3.2 SQP-based Algorithm for Finding the Optimal Solution of D-ECm	19
第4章	Heuristic Method	23
4.1 Problem Formulation for Simplifing D-ECm	23
4.2 DaTA-H Algorithm for S-ECM	25
第5章	Performance Evaluation	30
5.1 Comparison of Network’s Energy Consumption	32
5.2 Comparison of Network’s Sum-Throughput	37
5.3 Comparison of Network’s Energy Efficiency	41
5.4 Comparison of Failed Decoded Signals	45
第6章	Conclusion	48
參考文獻	49

 
圖目錄
圖 一、網路模型	8
圖 二、SWIPT技術之示意圖	9
圖 三、DT-SWIPT切換接收者之示意圖	10
圖 四、DT-SWIPT傳輸者之時間軸示意圖	11
圖 五、DT-SWIPT機制之訊框結構	12
圖 六、一般場景中網路能源消耗比較圖	33
圖 七、特殊場景中網路能源消耗比較圖	34
圖 八、不同α之一般場景中DaTA-H之網路能源消耗比較圖	36
圖 九、不同α之特殊場景中DaTA-H之網路能源消耗比較圖	36
圖 十、一般場景中網路總吞吐量比較圖	38
圖 十一、特殊場景中網路總吞吐量比較圖	38
圖 十二、不同α之一般場景中DaTA-H之網路總吞吐量比較圖	40
圖 十三、不同α之特殊場景中DaTA-H之網路總吞吐量比較圖	40
圖 十四、一般場景中網路能源效率比較圖	42
圖 十五、特殊場景中網路能源效率比較圖	43
圖 十六、不同α之一般場景中DaTA-H之能源效率比較圖	44
圖 十七、不同α之特殊場景中DaTA-H之能源效率比較圖	45
圖 十八、解析失敗之訊號數量比較圖。	46

表目錄
表 一、數據模擬之參數表格	31

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